E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference
Researchers have developed E-ReCON, a novel compute-in-memory (CIM) macro designed for efficient AI inference on edge devices. This macro utilizes a compact ReRAM bitcell capable of performing multiplication for both conventional neural networks and spiking neural networks. The design incorporates an interleaved adder tree to reduce transistor count and power consumption, achieving high energy efficiency and low latency. AI
IMPACT This new compute-in-memory macro could enable more powerful and energy-efficient AI processing directly on edge devices.